import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_SCREAMING_SNAKE_CASE 		=      1_6
_SCREAMING_SNAKE_CASE 		=      3_2





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_ = 16       )		->	List[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      AutoTokenizer.from_pretrained("""bert-base-cased"""       )
 UpperCamelCase			  =      load_dataset("""glue"""							,					"""mrpc"""       )

 def tokenize_function(UpperCamelCase_       ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCamelCase			  =      tokenizer(examples["""sentence1"""]							,					examples["""sentence2"""]							,					truncation=UpperCamelCase_							,					max_length=UpperCamelCase_       )
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  UpperCamelCase			  =      datasets.map(
      UpperCamelCase_							,					batched=UpperCamelCase_							,					remove_columns=["""idx""", """sentence1""", """sentence2"""]							,					)

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 UpperCamelCase			  =      tokenized_datasets.rename_column("""label"""							,					"""labels"""       )

 def collate_fn(UpperCamelCase_       ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCamelCase			  =      128 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   UpperCamelCase			  =      16
  elif accelerator.mixed_precision != "no":
   UpperCamelCase			  =      8
  else:
   UpperCamelCase			  =      None

  return tokenizer.pad(
      UpperCamelCase_							,					padding="""longest"""							,					max_length=UpperCamelCase_							,					pad_to_multiple_of=UpperCamelCase_							,					return_tensors="""pt"""							,					)

 # Instantiate dataloaders.
 UpperCamelCase			  =      DataLoader(
     tokenized_datasets["""train"""]							,					shuffle=UpperCamelCase_							,					collate_fn=UpperCamelCase_							,					batch_size=UpperCamelCase_							,					drop_last=UpperCamelCase_       )
 UpperCamelCase			  =      DataLoader(
     tokenized_datasets["""validation"""]							,					shuffle=UpperCamelCase_							,					collate_fn=UpperCamelCase_							,					batch_size=UpperCamelCase_							,					drop_last=(accelerator.mixed_precision == """fp8""")							,					)

 return train_dataloader, eval_dataloader





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialize accelerator
 UpperCamelCase			  =      Accelerator(cpu=args.cpu							,					mixed_precision=args.mixed_precision       )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCamelCase			  =      config["""lr"""]
 UpperCamelCase			  =      int(config["""num_epochs"""]       )
 UpperCamelCase			  =      int(config["""seed"""]       )
 UpperCamelCase			  =      int(config["""batch_size"""]       )

 UpperCamelCase			  =      evaluate.load("""glue"""							,					"""mrpc"""       )

 # If the batch size is too big we use gradient accumulation
 UpperCamelCase			  =      1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  UpperCamelCase			  =      batch_size // MAX_GPU_BATCH_SIZE
  UpperCamelCase			  =      MAX_GPU_BATCH_SIZE

 set_seed(UpperCamelCase_       )
 UpperCamelCase  ,     UpperCamelCase			  =      get_dataloaders(UpperCamelCase_							,					UpperCamelCase_       )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase			  =      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,					return_dict=UpperCamelCase_       )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 UpperCamelCase			  =      model.to(accelerator.device       )
 # Instantiate optimizer
 UpperCamelCase			  =      AdamW(params=model.parameters()							,					lr=UpperCamelCase_       )

 # Instantiate scheduler
 UpperCamelCase			  =      get_linear_schedule_with_warmup(
     optimizer=UpperCamelCase_							,					num_warmup_steps=100							,					num_training_steps=(len(UpperCamelCase_       ) * num_epochs) // gradient_accumulation_steps							,					)

 # Prepare everything
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.

 UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      accelerator.prepare(
     UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 # Now we train the model
 for epoch in range(UpperCamelCase_       ):
  model.train()
  for step, batch in enumerate(UpperCamelCase_       ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device       )
   UpperCamelCase			  =      model(**UpperCamelCase_       )
   UpperCamelCase			  =      outputs.loss
   UpperCamelCase			  =      loss / gradient_accumulation_steps
   accelerator.backward(UpperCamelCase_       )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  for step, batch in enumerate(UpperCamelCase_       ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device       )
   with torch.no_grad():
    UpperCamelCase			  =      model(**UpperCamelCase_       )
   UpperCamelCase			  =      outputs.logits.argmax(dim=-1       )
   UpperCamelCase  ,     UpperCamelCase			  =      accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
   metric.add_batch(
       predictions=UpperCamelCase_							,					references=UpperCamelCase_							,					)

  UpperCamelCase			  =      metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"""epoch {epoch}:"""							,					UpperCamelCase_       )





def 			lowercase(	)		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      argparse.ArgumentParser(description="""Simple example of training script."""       )
 parser.add_argument(
     """--mixed_precision"""							,					type=UpperCamelCase_							,					default=UpperCamelCase_							,					choices=["""no""", """fp16""", """bf16""", """fp8"""]							,					help="""Whether to use mixed precision. Choose"""
     """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
     """and an Nvidia Ampere GPU."""							,					)
 parser.add_argument("""--cpu"""							,					action="""store_true"""							,					help="""If passed, will train on the CPU."""       )
 UpperCamelCase			  =      parser.parse_args()
 UpperCamelCase			  =      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(UpperCamelCase_							,					UpperCamelCase_       )


if __name__ == "__main__":
     main()

import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
